Systems and methods for clinical decision support for lipid-lowering therapies for cardiovascular disease

Abstract

Provided herein are methods and systems for making patient-specific therapy recommendations of a lipid-lowering therapy for patients with known or suspected cardiovascular disease, such as atherosclerosis.

What is claimed is: 
     
       1. A computer-implemented method for clinical decision support, the method comprising:
 receiving non-invasively obtained data related to a plaque from a patient; 
 inputting the non-invasively obtained data from the patient to a neural network; 
 generating, as an output of the neural network, virtual 'omics data that include predicted pathway activation or molecule levels, or both, of the patient by applying the neural network to the non-invasively obtained data from the patient; 
 updating an in silico systems biology model using the generated virtual 'omics data to generate an in silico patient-specific systems biology model, wherein
 (i) the in silico systems biology model comprises a set of networks, wherein each network comprises a plurality of nodes, each node representing a baseline level of a molecule, and a plurality of edges between pairs of nodes, each edge representing a molecule-molecule interaction, 
 (ii) at least two of the nodes represent molecules whose levels are affected by atherosclerotic cardiovascular disease, and 
 (iii) at least one of the set of networks includes a disease-associated pathway activation or molecule level, or both, for each of the nodes in the network; 
 
 perturbing the in silico patient-specific systems biology model to simulate a therapeutic effect of a lipid-lowering agent for the patient; and 
 providing an output indicating a level of improvement in the atherosclerotic cardiovascular disease by the lipid-lowering agent for the patient and a recommendation supporting a clinical decision as to whether the lipid-lowering agent would benefit the patient. 
 
     
     
       2. The computer-implemented method of  claim 1 , wherein the recommendation informs a decision that leads to a clinical action. 
     
     
       3. The computer-implemented method of  claim 1 , wherein the recommendation enables a healthcare provider to tailor a therapy for the patient. 
     
     
       4. The computer-implemented method of  claim 1 , wherein the at least one of the set of networks includes nodes corresponding, respectively, to one or more of a glycosylated low-density lipoprotein (glyLDL), an oxidized LDL (oxLDL), a minimally-modified LDL (mmLDL), or a very-low-density lipoprotein (VLDL). 
     
     
       5. The computer-implemented method of  claim 1 , wherein the non-invasively obtained data is imaging data. 
     
     
       6. The computer-implemented method of  claim 5 , wherein the non-invasively obtained imaging data is obtained by computed tomography (CT), dual energy computed tomography (DECT), spectral computed tomography (spectral CT), computed tomography angiography (CTA), cardiac computed tomography angiography (CCTA), magnetic resonance imaging (MM), multi-contrast magnetic resonance imaging (multi-contrast MRI), ultrasound (US), positron emission tomography (PET), intra-vascular ultrasound (IVUS), optical coherence tomography (OCT), near-infrared radiation spectroscopy (NIRS), or single-photon emission tomography (SPECT) diagnostic images or any combination thereof. 
     
     
       7. The computer-implemented method of  claim 1 , wherein the molecule refers to a protein, a gene, or a metabolite. 
     
     
       8. The computer-implemented method of  claim 7 , wherein the at least one of the set of networks further comprises edges representing protein-protein interactions, gene-gene interactions, protein-metabolite interactions, and/or protein-gene interactions. 
     
     
       9. The computer-implemented method of  claim 8 , wherein interactions represent any one of translation, activation, inhibition, indirect effect, state change, binding, dissociation, phosphorylation, dephosphorylation, glycosylation, ubiquitination, and methylation as a result of an interaction between two molecules. 
     
     
       10. The computer-implemented method of  claim 1 , wherein the lipid-lowering agent is a hyperlipidemia control medication. 
     
     
       11. The computer-implemented method of  claim 10 , wherein the hyperlipidemia control medication is a statin. 
     
     
       12. The computer-implemented method of  claim 11 , wherein the statin is atorvastatin. 
     
     
       13. The computer-implemented method of  claim 10 , wherein the hyperlipidemia control medication is an intensive lipid-lowering agent. 
     
     
       14. The computer-implemented method of  claim 13 , wherein the intensive lipid lowering agent is a proprotein convertase subtilisin kexin type 9 (PCSK9) inhibitor or a cholesteryl ester transfer protein (CETP) inhibitor. 
     
     
       15. The computer-implemented method of  claim 10 , wherein the hyperlipidemia control medication is a hypertriglyceridemia lowering agent or a hypercholesterolemia lowering agent. 
     
     
       16. The computer-implemented method of  claim 15 , wherein the hypercholesterolemia lowering agent is a statin, ezetimibe, bile acid sequestrant, adenosine triphosphate-citrate lyase (ACL) inhibitor, fibrate, niacin, omega-3 fatty acid ethyl ester, or omega-3 polyunsaturated fatty acids (PUFA). 
     
     
       17. A clinical decision support system comprising:
 a memory configured to store instructions; and 
 a processor to execute the instructions to perform operations comprising:
 inputting, to a neural network, non-invasively obtained data related to a plaque from a patient; 
 generating virtual 'omics data that include predicted pathway activation or molecule levels, or both, of the patient, by applying the neural network to the non-invasively obtained data from the patient; 
 updating an in silico systems biology model using the generated virtual 'omics data to generate an in silico patient-specific systems biology model, wherein
 (i) the in silico systems biology model comprises a set of networks, wherein each network comprises a plurality of nodes, each node representing a baseline level of a molecule, and a plurality of edges between pairs of nodes, each edge representing a molecule-molecule interaction, 
 (ii) at least two of the nodes represent molecules whose levels are affected by the atherosclerotic cardiovascular disease, and 
 (iii) at least one of the set of networks includes a disease-associated pathway activation or molecule level, or both, for each of the nodes in the network; 
 
 perturbing the in silico patient-specific systems biology model to simulate a therapeutic effect of a lipid-lowering agent for the patient; and 
 providing an output indicating a level of improvement in the atherosclerotic cardiovascular disease by the lipid-lowering agent for the patient and a recommendation supporting a clinical decision as to whether the lipid-lowering agent would benefit the patient. 
 
 
     
     
       18. The clinical decision support system of  claim 17 , wherein the molecule refers to a protein, a gene, or a metabolite. 
     
     
       19. The clinical decision support system of  claim 18 , wherein the at least one of the set of networks further comprises edges representing protein-protein interactions, gene-gene interactions, protein-metabolite interactions, and/or protein-gene interactions. 
     
     
       20. The clinical decision support system of  claim 19 , wherein interactions represent any one of translation, activation, inhibition, indirect effect, state change, binding, dissociation, phosphorylation, dephosphorylation, glycosylation, ubiquitination, and methylation as a result of an interaction between two molecules. 
     
     
       21. One or more non-transitory computer-readable media storing instructions that are executable by a processing device, and upon such execution cause the processing device to perform operations comprising:
 inputting, to a neural network, non-invasively obtained data related to a plaque from a patient; 
 generating virtual 'omics data that include predicted pathway activation or molecule levels, or both, of the patient, by applying the neural network to the non-invasively obtained data related from the patient; 
 updating an in silico systems biology model using the generated virtual 'omics data to generate an in silico patient-specific systems biology model, wherein
 (i) the in silico systems biology model comprises a set of networks, wherein each network comprises a plurality of nodes, each node representing a baseline level of a molecule, and a plurality of edges between pairs of nodes, each edge representing a molecule-molecule interaction, 
 (ii) at least two of the nodes represent proteins whose levels are affected by the atherosclerotic cardiovascular disease, and 
 (iii) at least one of the set of networks includes a disease-associated pathway activation or molecule level, or both, for each of the nodes in the network; 
 
 perturbing the in silico patient-specific systems biology model to simulate a therapeutic effect of a lipid-lowering agent for the patient; and 
 providing an output indicating a level of improvement in the atherosclerotic cardiovascular disease by the lipid-lowering agent for the patient and a recommendation supporting a clinical decision as to whether the lipid-lowering agent would benefit the patient. 
 
     
     
       22. A computer-implemented method for clinical decision support of  claim 1 ,
 wherein the plaque comprises atherosclerotic plaque. 
 
     
     
       23. A clinical decision support system of  claim 17 , wherein the plaque comprises atherosclerotic plaque. 
     
     
       24. The one or more non-transitory computer-readable media of  claim 21 , wherein the plaque comprises atherosclerotic plaque.